Processing Data with the objetive of designing metrics for team performance - Open17 Climate Gender

  1. Read the data files

library(readxl)
library(tidyr)
library(dplyr)
library(ggplot2)
library(plotly)

source("../../Accelerate/notebooks/custom_functions.R")

teams = read_excel("../data/O17ClimateGender_teamformation.xlsx")
#teams = lapply(teams, as.character)

assesment = read_excel("../data/O17ClimateGender_assessment.xlsx")

load("../../Accelerate/processed data/registration.RData")

map = read.csv("../../Accelerate/data/team_users_hashed.csv", stringsAsFactors = FALSE)
colnames(map) = c("Team", "ID", "Hash", "Mentors")

reg = merge(reg, map[,c("Team", "Hash")], by.x = "ID", by.y = "Hash")

reg_map = c("A2: Women & Technology Against Climate Change" = "T6: Women & Technology Against Climate Change", "B2: TEAM FOILED" = "T3: TEAM FOILED", "C1: Andapé Institute" = "T13: Andapé Institute", "C3: WOMER" = "T5: WOMER", "A5: Donate Water Project" = "T9: DonateWater", "B5: Rights of Climate" = "T11: Rights of Climate", "B3: Eco Winners" = "T14: Eco Winners", "B4: Women 4 Sustainable World" = "T12: Women 4 Sustainable World", "A1: Up Get App/CitiCERN" = "T7: UpGet app - CitiCERN Project", "B1: Water Warriors" = "T10: Water Warriors", "C2: PAM" = "T4: PAM", "C4: Climate Gender Justice" = "T8: Climate Gender Justice", "A3: Rhythm of Bamboos" = "T1: SDesiGn (Old name: Rhythm of Bamboos)", "C5: Ashifa Nazrin" = "C5: Ashifa Nazrin", "A4: Flood Rangers" = "T2: Flood Rangers")

reg_map = data.frame(old_name = names(reg_map), new_name = reg_map)
reg = merge(reg, reg_map, by.x = "Team", by.y = "old_name", all.x = TRUE)

write.csv(unnest(reg, cols = c("communication")), file = "../processed data/reg_edited.csv")

map = merge(map, reg_map, by.x = "Team", by.y = "old_name", all.x = TRUE)
map$new_name = as.character(map$new_name)
map$new_name[map$Team == "Organizing Team"] = "Organizing Team"

Outcome Variable

  1. Outcome end of Evaluate

outcome = assesment[,c("Team", "Total", "Weekly Evaluation", "Commitment", "Attendance", "Deliverables")]
outcome = merge(outcome, teams[,c("Team Name", "Stage")], by.x = "Team", by.y = "Team Name", all.x = TRUE)
outcome$Stage = factor(outcome$Stage, levels = c("Evaluate", "Accelerate", "Refine"), ordered = TRUE)
  1. Surveys and Interactions

load("../../Evaluate/processed data/surveys.RData")

inter = interactions[,c(1,8,2,3)]
inter = merge(inter, map[,c("ID", "new_name")], by.x = "user_id", by.y = "ID", all.x = TRUE)
colnames(inter) = c("From", "To", "Survey_id", "Question", "From_team")
inter = merge(inter, map[,c("ID", "new_name")], by.x = "To", by.y = "ID", all.x = TRUE)
colnames(inter)[colnames(inter) == "new_name"] = "To_team"

inter = inter[!inter$To %in% c(34), c(2,1,3,4,5,6)]

g_int_teams = graph_from_data_frame(inter[,c(5,6,1:4)], directed = TRUE, vertices = teams)
E(g_int_teams)$weight = 1
g_int_teams_simp = simplify(g_int_teams, remove.loops = FALSE)
  1. Create an Extensive dataframe with different things that can be calculated with the Interaction data

Listing them out here (by Team)

  1. of responses

  2. in-degree (self, from other teams) + normalised (proportion of in edges to self/others etc.)
  3. out-degree (org team, other teams) + normalised
  4. …

inter_pr = inter %>% group_by(From_team, To_team) %>% summarise(weight = n())
`summarise()` has grouped output by 'From_team'. You can override using the `.groups` argument.
# Responses, Out degree to Org Team, Other peers, Self

temp = inter_pr %>% group_by(From_team) %>% summarise(no_responses = n(), self_interactions = weight[To_team == From_team], org_interactions = weight[To_team == "Organizing Team"], peers_out = sum(weight[!To_team %in% c(From_team, "Organizing Team")]))

stats = temp

# In-degree from other peers

temp = inter_pr %>% group_by(To_team) %>% summarise(peers_in = sum(weight[!From_team %in% c(To_team, "Organizing Team")]))
stats = merge(stats, temp, by.x = "From_team", by.y = "To_team", all.x = TRUE, all.y = TRUE)

stats$self_interactions_norm = 2*stats$self_interactions/(2*stats$self_interactions + stats$peers_in + stats$org_interactions + stats$peers_out)
stats$org_interactions_norm = stats$org_interactions/(2*stats$self_interactions + stats$peers_in + stats$org_interactions + stats$peers_out)
stats$peers_out_norm = stats$peers_out/(2*stats$self_interactions + stats$peers_in + stats$org_interactions + stats$peers_out)
stats$peers_in_norm = stats$peers_in/(2*stats$self_interactions + stats$peers_in + stats$org_interactions + stats$peers_out)

stats = stats[!stats$From_team %in% c("Organizing Team"),]

Slack Interactions


load("../../Evaluate/processed data/slack_all_int.RData")

inter_sl = df_total %>% group_by(From_Team, To_Team) %>% summarise(weight = n())
`summarise()` has grouped output by 'From_Team'. You can override using the `.groups` argument.
colnames(inter_sl) = c("From_team", "To_team", "weight")

temp_1 = inter_sl %>% group_by(From_team) %>% summarise(slack_self_interactions = weight[To_team == From_team], slack_org_out = weight[To_team == "Organizing Team"], slack_peers_out = sum(weight[!To_team %in% c(From_team, "Organizing Team")]))
`summarise()` has grouped output by 'From_team'. You can override using the `.groups` argument.
temp_2 = inter_sl %>% group_by(To_team) %>% summarise(slack_peers_in = sum(weight[!From_team %in% c(To_team, "Organizing Team")]), slack_org_in = weight[From_team == "Organizing Team"])

slack_stats = merge(temp_1, temp_2, by.x = "From_team", by.y = "To_team", all.x = TRUE, all.y = TRUE)
slack_stats = slack_stats[!slack_stats$From_team %in% c("Organizing Team", "Tool Owner"),]

slack_stats = merge(slack_stats, reg_map, by.x = "From_team", by.y = "old_name", all.x = TRUE)
slack_stats$From_team = slack_stats$new_name
slack_stats = slack_stats %>% select(-new_name)
slack_stats[is.na(slack_stats)] = 0


slack_stats$slack_self_interactions_norm = 2*slack_stats$slack_self_interactions/(2*slack_stats$slack_self_interactions + slack_stats$slack_org_out + slack_stats$slack_peers_out + slack_stats$slack_peers_in + slack_stats$slack_org_in)

slack_stats$slack_org_out_norm = slack_stats$slack_org_out/(2*slack_stats$slack_self_interactions + slack_stats$slack_org_out + slack_stats$slack_peers_out + slack_stats$slack_peers_in + slack_stats$slack_org_in)
slack_stats$slack_peers_out_norm = slack_stats$slack_peers_out/(2*slack_stats$slack_self_interactions + slack_stats$slack_org_out + slack_stats$slack_peers_out + slack_stats$slack_peers_in + slack_stats$slack_org_in)
slack_stats$slack_peers_in_norm = slack_stats$slack_peers_in/(2*slack_stats$slack_self_interactions + slack_stats$slack_org_out + slack_stats$slack_peers_out + slack_stats$slack_peers_in + slack_stats$slack_org_in)
slack_stats$slack_org_in_norm = slack_stats$slack_org_in/(2*slack_stats$slack_self_interactions + slack_stats$slack_org_out + slack_stats$slack_peers_out + slack_stats$slack_peers_in + slack_stats$slack_org_in)

Merge


interaction_stats = merge(stats, slack_stats, by.x = "From_team", by.y = "From_team", all.x = TRUE, all.y = TRUE)

Network Properties


network_stats = data.frame(nodes = V(g_int_teams_simp)$name, strength_in = strength(g_int_teams_simp, mode = "in"), strength_out = strength(g_int_teams_simp, mode = "out"), betweenness = betweenness(g_int_teams_simp, weights = 1/E(g_int_teams_simp)$weight, normalized = TRUE), burt = constraint(g_int_teams_simp, weights = E(g_int_teams_simp)$weight))

  1. Thinking about Metrics
  1. Descriptive
  1. Gender Diversity
  2. Assembled/Self organised
  3. Background
  4. SDG experience
  1. Interactions
  1. Interaction with ORG
  2. Interaction with Team members
  3. Interaction with other teams
  1. Tasks

  1. Processing Reg. file to make teamwise diversity metrics

Entropy - low score for less diversity (Higher for more diversity)


shannon = function(list)
{
  ent = 0
  for (i in unique(list))
  {
    t = sum(list == i)
    n = length(list)
    ent = ent + (t/n)*log(t/n)
  }
  
  return(-1*ent)
}

simpson = function(list)
{
  ent = 0
  for (i in unique(list))
  {
    t = sum(list == i)
    n = length(list)
    ent = ent + (t/n)*(t/n)
  }
  
  return(1/ent)
}

metrics = data.frame(Team = unique(reg$new_name))

for (i in c("gender", "country_orig", "country_resid", "education", "communication", "exante_project_SDG", "background", "occupation"))
{
  temp = reg[,c("new_name", i)]
  temp = clean_split_mcq(temp)
  colnames(temp) = c("Team", "var")
  
#  num = temp$var
  
  t = temp %>% group_by(Team) %>% summarise(shannon = shannon(var), simpson = simpson(var))
  colnames(t) = c("Team", paste(i, "_shannon", sep = ''), paste(i, "_simpson", sep = ''))
  
  metrics = merge(metrics, t, by.x = "Team", by.y = "Team", all.x = TRUE, all.y = TRUE)
  
}

metrics = merge(metrics, teams, by.x = "Team", by.y = "Team Name", all.x = TRUE)

metrics_score = merge(metrics, assesment, by.x = "Team", by.y = "Team", all.x = TRUE, all.y = TRUE)

library(corrplot)

#metrics_score$`Final Pitch` = as.numeric(metrics_score$`Final Pitch`)
#M = cor(metrics_score[,c(2,4,6,8,10,14,16,19,21,24:27,31)], use = "complete.obs")
#corrplot(M, method = 'number') # colorful number

temp = merge(interaction_stats, outcome, by.x = "From_team", by.y = "Team", all.x = TRUE, all.y = TRUE)
M = cor(temp[,c(2:(ncol(temp)-1))], use = "complete.obs")
corrplot(M, method = "number")

Cleaner Version


outcome = c("Final Pitch", "Appropriateness of Methodology", "Weekly Evaluation", "Commitment", "Attendance", "Deliverables", "Sum", "Total")

df_corr = data.frame()

for (i in outcome)
{
  for (j in colnames(interaction_stats))
  {
    if(!j %in% c("From_team", "no_responses"))
    {
      c = cor.test(temp[,j], temp[,i])
      
      df_corr = rbind(df_corr, data.frame(i = i, j = j, cor = c$estimate, p_val = c$p.value))
      
    }
  }
}

df_corr$cor[df_corr$p_val > 0.1] = 0
df_corr$cor = round(df_corr$cor, 3)

plt = ggplot(df_corr) + geom_tile(aes(x = i, y = j, fill = cor), lwd = 1.5, linetype = 1) + scale_fill_gradient2(low = "blue", high = "red") + theme_bw() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank()) + ylab("") + xlab("") + geom_text(aes(x = i, y = j, label = cor))

ggplotly(plt)

Network Props


temp = merge(assesment, network_stats, by.x = "Team", by.y = "nodes", all.x = TRUE, all.y = TRUE)
temp = temp[!temp$Team == "Organizing Team",]

outcome = c("Final Pitch", "Appropriateness of Methodology", "Weekly Evaluation", "Commitment", "Attendance", "Deliverables", "Sum", "Total")

df_corr = data.frame()

for (i in outcome)
{
  for (j in colnames(network_stats))
  {
    if(!j %in% c("nodes"))
    {
      c = cor.test(temp[,j], temp[,i])
      
      df_corr = rbind(df_corr, data.frame(i = i, j = j, cor = c$estimate, p_val = c$p.value))
      
    }
  }
}


df_corr$cor[df_corr$p_val > 0.1] = 0
df_corr$cor = round(df_corr$cor, 3)

plt = ggplot(df_corr) + geom_tile(aes(x = i, y = j, fill = cor), lwd = 1.5, linetype = 1) + scale_fill_gradient2(low = "blue", high = "red") + theme_bw() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank()) + ylab("") + xlab("") + geom_text(aes(x = i, y = j, label = cor))

ggplotly(plt)

Diversity


inter = interactions[,c(1,8,2,3)]
inter = merge(inter, map[,c("ID", "Team")], by.x = "user_id", by.y = "ID", all.x = TRUE)
colnames(inter) = c("From", "To", "Survey_id", "Question", "From_team")
inter = merge(inter, map[,c("ID", "Team")], by.x = "To", by.y = "ID", all.x = TRUE)
colnames(inter)[colnames(inter) == "Team"] = "To_team"

inter = inter[!inter$To %in% c(34), c(2,1,3,4,5,6)]

g_int_teams = graph_from_data_frame(inter[,c(5,6,1:4)], directed = TRUE)
E(g_int_teams)$weight = 1
g_int_teams_simp = simplify(g_int_teams, remove.loops = FALSE)

Some Teamwise Network Properties


inter = interactions[,c(1,8,2,3)]
inter = merge(inter, map[,c("ID", "Team")], by.x = "user_id", by.y = "ID", all.x = TRUE)
colnames(inter) = c("From", "To", "Survey_id", "Question", "From_team")
inter = merge(inter, map[,c("ID", "Team")], by.x = "To", by.y = "ID", all.x = TRUE)
colnames(inter)[colnames(inter) == "Team"] = "To_team"

inter = inter[!inter$To %in% c(34), c(2,1,3,4,5,6)]

g_int_teams = graph_from_data_frame(inter[,c(5,6,1:4)], directed = TRUE)
E(g_int_teams)$weight = 1
g_int_teams_simp = simplify(g_int_teams, remove.loops = FALSE)

Network Metrics

  1. Weighted interactions (Other teams, self, ORG)
  2. Normalised weighted interactions - total = 1

process_interactions = function(inter)
{
  temp = inter %>% group_by(From_team, To_team) %>% summarise(weight = n())
  temp_out = temp %>% group_by(From_team) %>% summarise(self = weight[To_team == From_team], org_out = weight[To_team == "Organizing Team"], peers_out = sum(weight[!To_team %in% c(From_team, "Organizing Team")]))

  temp_in = temp %>% group_by(To_team) %>% summarise(peers_in = sum(weight[!From_team == To_team]))

  degr = merge(temp_out, temp_in, by.x = "From_team", by.y = "To_team", all.x = TRUE, all.y = TRUE)
  colnames(degr)[colnames(degr) == "From_team"] = "Team"

  degr = degr[!degr$Team == "Organizing Team",]
  return(degr)
}
pdf("../figures/stats_team_type_box.pdf")

for (i in colnames(degr_as))
{
  if(!i %in% c("new_name", "Team", "Type", "Geographic Location", "Gender F/M", "Stage"))
  {
    temp = degr_as[,c("Type", i)]
    colnames(temp) = c("Type", "var")
    #t = temp %>% group_by(Type) %>% summarise(mean = mean(var), se = se(var))
    
    plt = ggplot(temp, aes(x = Type, y = var)) + geom_boxplot() + theme_bw(base_size = 20) + xlab("") + ylab("") + ggtitle(i) + geom_point(aes(x = Type, y = var), alpha = 0.3)
    
    print(plt)
    
  }
}
Warning: Removed 8 rows containing non-finite values (stat_boxplot).
Warning: Removed 8 rows containing missing values (geom_point).
Warning: Removed 4 rows containing non-finite values (stat_boxplot).
Warning: Removed 4 rows containing missing values (geom_point).
dev.off()
null device 
          1 

Pre-Formed vs Assembled

  1. All Interactions


for (j in unique(inter$Question))
{  
  
  a = process_interactions(inter[inter$Question == j,])
  a = merge(a, reg_map, by.x = "Team", by.y = "old_name")
  a = merge(a, teams, by.x = "new_name", by.y = "Team Name")
  
  pdf(paste("../figures/", j, "_box.pdf", sep = ""))
  
  for (i in colnames(a))
  {
    if(!i %in% c("new_name", "Team", "Type", "Stage"))
    {
      temp = a[,c("Type", i)]
      colnames(temp) = c("Type", "var")
      plt = ggplot(temp, aes(x = Type, y = var)) + geom_boxplot() + theme_bw(base_size = 20) + xlab("") + ylab("") + ggtitle(i) + geom_point(aes(x = Type, y = var), alpha = 0.3)
      
      print(plt)
      
    }
  }
  
  dev.off()
  
pdf(paste("../figures/", j, "_bar.pdf", sep = ""))

for (i in colnames(a))
{
  if(!i %in% c("new_name", "Team", "Type", "Geographic Location", "Gender F/M", "Stage"))
  {
    temp = a[,c("Type", i)]
    colnames(temp) = c("Type", "var")
    t = temp %>% group_by(Type) %>% summarise(mean = mean(var), se = se(var))
    
    plt = ggplot(t, aes(x = Type, y = mean)) + geom_bar(stat = "identity") + geom_errorbar(aes(ymin = mean-se, ymax = mean+se), width = 0) + theme_bw(base_size = 20) + xlab("") + ylab("") + ggtitle(i)
    
    print(plt)
    
  }
}

dev.off()

}
`summarise()` has grouped output by 'From_team'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'From_team'. You can override using the `.groups` argument.
Warning: Removed 13 rows containing non-finite values (stat_boxplot).
Warning: Removed 13 rows containing missing values (geom_point).
Warning: Removed 13 rows containing non-finite values (stat_boxplot).
Warning: Removed 13 rows containing missing values (geom_point).
Warning: Removed 13 rows containing non-finite values (stat_boxplot).
Warning: Removed 13 rows containing missing values (geom_point).
Warning: Removed 2 rows containing missing values (position_stack).
Warning: Removed 2 rows containing missing values (position_stack).
Warning: Removed 2 rows containing missing values (position_stack).
`summarise()` has grouped output by 'From_team'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'From_team'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'From_team'. You can override using the `.groups` argument.
`summarise()` has grouped output by 'From_team'. You can override using the `.groups` argument.
Warning: Removed 3 rows containing non-finite values (stat_boxplot).
Warning: Removed 3 rows containing missing values (geom_point).
Warning: Removed 3 rows containing non-finite values (stat_boxplot).
Warning: Removed 3 rows containing missing values (geom_point).
Warning: Removed 3 rows containing non-finite values (stat_boxplot).
Warning: Removed 3 rows containing missing values (geom_point).
Warning: Removed 1 rows containing missing values (position_stack).
Warning: Removed 1 rows containing missing values (position_stack).
Warning: Removed 1 rows containing missing values (position_stack).
  1. Specific Interactions

pdf("../figures/stats_team_progress_box.pdf")

for (i in colnames(degr_as))
{
  if(!i %in% c("new_name", "Team", "Type", "Geographic Location", "Gender F/M", "Stage"))
  {
    temp = degr_as[,c("Stage", i)]
    colnames(temp) = c("Type", "var")
    #t = temp %>% group_by(Type) %>% summarise(mean = mean(var), se = se(var))
    
    plt = ggplot(temp, aes(x = Type, y = var)) + geom_boxplot() + theme_bw(base_size = 20) + xlab("") + ylab("") + ggtitle(i) + geom_point(aes(x = Type, y = var), alpha = 0.3)
    
    print(plt)
    
  }
}
Warning: Removed 8 rows containing non-finite values (stat_boxplot).
Warning: Removed 8 rows containing missing values (geom_point).
Warning: Removed 4 rows containing non-finite values (stat_boxplot).
Warning: Removed 4 rows containing missing values (geom_point).
dev.off()
null device 
          1 
pdf("../figures/stats_team_progress_bar.pdf")

for (i in colnames(degr_as))
{
  if(!i %in% c("new_name", "Team", "Type", "Geographic Location", "Gender F/M", "Stage"))
  {
    temp = degr_as[,c("Stage", i)]
    colnames(temp) = c("Type", "var")
    t = temp %>% group_by(Type) %>% summarise(mean = mean(var), se = se(var))
    
    plt = ggplot(t, aes(x = Type, y = mean)) + geom_bar(stat = "identity") + geom_errorbar(aes(ymin = mean-se, ymax = mean+se), width = 0) + theme_bw(base_size = 20) + xlab("") + ylab("") + ggtitle(i)
    
    print(plt)
    
  }
}
Warning: Removed 1 rows containing missing values (position_stack).
Warning: Removed 1 rows containing missing values (position_stack).
dev.off()
null device 
          1 


pdf("../figures/stats_team_progress_box.pdf")

for (i in colnames(degr_as))
{
  if(!i %in% c("new_name", "Team", "Type", "Geographic Location", "Gender F/M", "Stage"))
  {
    temp = degr_as[,c("Stage", i)]
    colnames(temp) = c("Type", "var")
    #t = temp %>% group_by(Type) %>% summarise(mean = mean(var), se = se(var))
    
    plt = ggplot(temp, aes(x = Type, y = var)) + geom_boxplot() + theme_bw(base_size = 20) + xlab("") + ylab("") + ggtitle(i) + geom_point(aes(x = Type, y = var), alpha = 0.3)
    
    print(plt)
    
  }
}

dev.off()


pdf("../figures/stats_team_progress_bar.pdf")

for (i in colnames(degr_as))
{
  if(!i %in% c("new_name", "Team", "Type", "Geographic Location", "Gender F/M", "Stage"))
  {
    temp = degr_as[,c("Stage", i)]
    colnames(temp) = c("Type", "var")
    t = temp %>% group_by(Type) %>% summarise(mean = mean(var), se = se(var))
    
    plt = ggplot(t, aes(x = Type, y = mean)) + geom_bar(stat = "identity") + geom_errorbar(aes(ymin = mean-se, ymax = mean+se), width = 0) + theme_bw(base_size = 20) + xlab("") + ylab("") + ggtitle(i)
    
    print(plt)
    
  }
}

dev.off()
---
title: "R Notebook"
output: html_notebook
---

Processing Data with the objetive of designing metrics for team performance - Open17 Climate Gender


1. Read the data files

```{r}

library(readxl)
library(tidyr)
library(dplyr)
library(ggplot2)
library(plotly)

source("../../Accelerate/notebooks/custom_functions.R")

teams = read_excel("../data/O17ClimateGender_teamformation.xlsx")
#teams = lapply(teams, as.character)

assesment = read_excel("../data/O17ClimateGender_assessment.xlsx")

load("../../Accelerate/processed data/registration.RData")

map = read.csv("../../Accelerate/data/team_users_hashed.csv", stringsAsFactors = FALSE)
colnames(map) = c("Team", "ID", "Hash", "Mentors")

reg = merge(reg, map[,c("Team", "Hash")], by.x = "ID", by.y = "Hash")

reg_map = c("A2: Women & Technology Against Climate Change" = "T6: Women & Technology Against Climate Change", "B2: TEAM FOILED" = "T3: TEAM FOILED", "C1: Andapé Institute" = "T13: Andapé Institute", "C3: WOMER" = "T5: WOMER", "A5: Donate Water Project" = "T9: DonateWater", "B5: Rights of Climate" = "T11: Rights of Climate", "B3: Eco Winners" = "T14: Eco Winners", "B4: Women 4 Sustainable World" = "T12: Women 4 Sustainable World", "A1: Up Get App/CitiCERN" = "T7: UpGet app - CitiCERN Project", "B1: Water Warriors" = "T10: Water Warriors", "C2: PAM" = "T4: PAM", "C4: Climate Gender Justice" = "T8: Climate Gender Justice", "A3: Rhythm of Bamboos" = "T1: SDesiGn (Old name: Rhythm of Bamboos)", "C5: Ashifa Nazrin" = "C5: Ashifa Nazrin", "A4: Flood Rangers" = "T2: Flood Rangers")

reg_map = data.frame(old_name = names(reg_map), new_name = reg_map)
reg = merge(reg, reg_map, by.x = "Team", by.y = "old_name", all.x = TRUE)

write.csv(unnest(reg, cols = c("communication")), file = "../processed data/reg_edited.csv")

map = merge(map, reg_map, by.x = "Team", by.y = "old_name", all.x = TRUE)
map$new_name = as.character(map$new_name)
map$new_name[map$Team == "Organizing Team"] = "Organizing Team"

```

Outcome Variable

1. Outcome end of Evaluate

```{r}

outcome = assesment[,c("Team", "Total", "Weekly Evaluation", "Commitment", "Attendance", "Deliverables")]
outcome = merge(outcome, teams[,c("Team Name", "Stage")], by.x = "Team", by.y = "Team Name", all.x = TRUE)
outcome$Stage = factor(outcome$Stage, levels = c("Evaluate", "Accelerate", "Refine"), ordered = TRUE)

```


2. Surveys and Interactions

```{r}

load("../../Evaluate/processed data/surveys.RData")

inter = interactions[,c(1,8,2,3)]
inter = merge(inter, map[,c("ID", "new_name")], by.x = "user_id", by.y = "ID", all.x = TRUE)
colnames(inter) = c("From", "To", "Survey_id", "Question", "From_team")
inter = merge(inter, map[,c("ID", "new_name")], by.x = "To", by.y = "ID", all.x = TRUE)
colnames(inter)[colnames(inter) == "new_name"] = "To_team"

inter = inter[!inter$To %in% c(34), c(2,1,3,4,5,6)]

g_int_teams = graph_from_data_frame(inter[,c(5,6,1:4)], directed = TRUE, vertices = teams)
E(g_int_teams)$weight = 1
g_int_teams_simp = simplify(g_int_teams, remove.loops = FALSE)


```


3. Create an Extensive dataframe with different things that can be calculated with the Interaction data

Listing them out here (by Team)

1. # of responses
2. in-degree (self, from other teams) + normalised (proportion of in edges to self/others etc.)
2. out-degree (org team, other teams) + normalised
3. ...


```{r}

inter_pr = inter %>% group_by(From_team, To_team) %>% summarise(weight = n())

# Responses, Out degree to Org Team, Other peers, Self

temp = inter_pr %>% group_by(From_team) %>% summarise(no_responses = n(), self_interactions = weight[To_team == From_team], org_interactions = weight[To_team == "Organizing Team"], peers_out = sum(weight[!To_team %in% c(From_team, "Organizing Team")]))

stats = temp

# In-degree from other peers

temp = inter_pr %>% group_by(To_team) %>% summarise(peers_in = sum(weight[!From_team %in% c(To_team, "Organizing Team")]))
stats = merge(stats, temp, by.x = "From_team", by.y = "To_team", all.x = TRUE, all.y = TRUE)

stats$self_interactions_norm = 2*stats$self_interactions/(2*stats$self_interactions + stats$peers_in + stats$org_interactions + stats$peers_out)
stats$org_interactions_norm = stats$org_interactions/(2*stats$self_interactions + stats$peers_in + stats$org_interactions + stats$peers_out)
stats$peers_out_norm = stats$peers_out/(2*stats$self_interactions + stats$peers_in + stats$org_interactions + stats$peers_out)
stats$peers_in_norm = stats$peers_in/(2*stats$self_interactions + stats$peers_in + stats$org_interactions + stats$peers_out)

stats = stats[!stats$From_team %in% c("Organizing Team"),]

```


Slack Interactions

```{r}

load("../../Evaluate/processed data/slack_all_int.RData")

inter_sl = df_total %>% group_by(From_Team, To_Team) %>% summarise(weight = n())
colnames(inter_sl) = c("From_team", "To_team", "weight")

temp_1 = inter_sl %>% group_by(From_team) %>% summarise(slack_self_interactions = weight[To_team == From_team], slack_org_out = weight[To_team == "Organizing Team"], slack_peers_out = sum(weight[!To_team %in% c(From_team, "Organizing Team")]))

temp_2 = inter_sl %>% group_by(To_team) %>% summarise(slack_peers_in = sum(weight[!From_team %in% c(To_team, "Organizing Team")]), slack_org_in = weight[From_team == "Organizing Team"])

slack_stats = merge(temp_1, temp_2, by.x = "From_team", by.y = "To_team", all.x = TRUE, all.y = TRUE)
slack_stats = slack_stats[!slack_stats$From_team %in% c("Organizing Team", "Tool Owner"),]

slack_stats = merge(slack_stats, reg_map, by.x = "From_team", by.y = "old_name", all.x = TRUE)
slack_stats$From_team = slack_stats$new_name
slack_stats = slack_stats %>% select(-new_name)
slack_stats[is.na(slack_stats)] = 0


slack_stats$slack_self_interactions_norm = 2*slack_stats$slack_self_interactions/(2*slack_stats$slack_self_interactions + slack_stats$slack_org_out + slack_stats$slack_peers_out + slack_stats$slack_peers_in + slack_stats$slack_org_in)

slack_stats$slack_org_out_norm = slack_stats$slack_org_out/(2*slack_stats$slack_self_interactions + slack_stats$slack_org_out + slack_stats$slack_peers_out + slack_stats$slack_peers_in + slack_stats$slack_org_in)
slack_stats$slack_peers_out_norm = slack_stats$slack_peers_out/(2*slack_stats$slack_self_interactions + slack_stats$slack_org_out + slack_stats$slack_peers_out + slack_stats$slack_peers_in + slack_stats$slack_org_in)
slack_stats$slack_peers_in_norm = slack_stats$slack_peers_in/(2*slack_stats$slack_self_interactions + slack_stats$slack_org_out + slack_stats$slack_peers_out + slack_stats$slack_peers_in + slack_stats$slack_org_in)
slack_stats$slack_org_in_norm = slack_stats$slack_org_in/(2*slack_stats$slack_self_interactions + slack_stats$slack_org_out + slack_stats$slack_peers_out + slack_stats$slack_peers_in + slack_stats$slack_org_in)


```

Merge

```{r}

interaction_stats = merge(stats, slack_stats, by.x = "From_team", by.y = "From_team", all.x = TRUE, all.y = TRUE)

```


Network Properties

```{r}

network_stats = data.frame(nodes = V(g_int_teams_simp)$name, strength_in = strength(g_int_teams_simp, mode = "in"), strength_out = strength(g_int_teams_simp, mode = "out"), betweenness = betweenness(g_int_teams_simp, weights = 1/E(g_int_teams_simp)$weight, normalized = TRUE), burt = constraint(g_int_teams_simp, weights = E(g_int_teams_simp)$weight))

```



************************************************************************

3. Thinking about Metrics

a. Descriptive
  i) Gender Diversity
  ii) Assembled/Self organised
  iii) Background
  iv) SDG experience
  v) 
  
b. Interactions
  i) Interaction with ORG
  ii) Interaction with Team members
  iii) Interaction with other teams
  
c. Tasks

**********************************

a) Processing Reg. file to make teamwise diversity metrics

Entropy - low score for less diversity (Higher for more diversity)

```{r}

shannon = function(list)
{
  ent = 0
  for (i in unique(list))
  {
    t = sum(list == i)
    n = length(list)
    ent = ent + (t/n)*log(t/n)
  }
  
  return(-1*ent)
}

simpson = function(list)
{
  ent = 0
  for (i in unique(list))
  {
    t = sum(list == i)
    n = length(list)
    ent = ent + (t/n)*(t/n)
  }
  
  return(1/ent)
}

```


```{r}

metrics = data.frame(Team = unique(reg$new_name))

for (i in c("gender", "country_orig", "country_resid", "education", "communication", "exante_project_SDG", "background", "occupation"))
{
  temp = reg[,c("new_name", i)]
  temp = clean_split_mcq(temp)
  colnames(temp) = c("Team", "var")
  
#  num = temp$var
  
  t = temp %>% group_by(Team) %>% summarise(shannon = shannon(var), simpson = simpson(var))
  colnames(t) = c("Team", paste(i, "_shannon", sep = ''), paste(i, "_simpson", sep = ''))
  
  metrics = merge(metrics, t, by.x = "Team", by.y = "Team", all.x = TRUE, all.y = TRUE)
  
}

metrics = merge(metrics, teams, by.x = "Team", by.y = "Team Name", all.x = TRUE)

metrics_score = merge(metrics, assesment, by.x = "Team", by.y = "Team", all.x = TRUE, all.y = TRUE)

```

```{r}

library(corrplot)

#metrics_score$`Final Pitch` = as.numeric(metrics_score$`Final Pitch`)
#M = cor(metrics_score[,c(2,4,6,8,10,14,16,19,21,24:27,31)], use = "complete.obs")
#corrplot(M, method = 'number') # colorful number

temp = merge(interaction_stats, outcome, by.x = "From_team", by.y = "Team", all.x = TRUE, all.y = TRUE)
M = cor(temp[,c(2:(ncol(temp)-1))], use = "complete.obs")
corrplot(M, method = "number")
```

Cleaner Version

```{r}

#outcome = c("Final Pitch", "Appropriateness of Methodology", "Weekly Evaluation", "Commitment", "Attendance", "Deliverables", "Sum", "Total")

df_corr = data.frame()

for (i in colnames(outcome))
{
  if(! i %in% c("Team", "Stage"))
  {
    for (j in colnames(interaction_stats))
    {
      if(!j %in% c("From_team", "no_responses"))
      {
        c = cor.test(temp[,j], temp[,i])
        
        df_corr = rbind(df_corr, data.frame(i = i, j = j, cor = c$estimate, p_val = c$p.value))
        
      }
    }
  }
}

```

```{r}

df_corr$cor[df_corr$p_val > 0.1] = 0
df_corr$cor = round(df_corr$cor, 3)

plt = ggplot(df_corr) + geom_tile(aes(x = i, y = j, fill = cor), lwd = 1.5, linetype = 1) + scale_fill_gradient2(low = "blue", high = "red") + theme_bw() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank()) + ylab("") + xlab("") + geom_text(aes(x = i, y = j, label = cor))

ggplotly(plt)

```
Network Props

```{r}

temp = merge(assesment, network_stats, by.x = "Team", by.y = "nodes", all.x = TRUE, all.y = TRUE)
temp = temp[!temp$Team == "Organizing Team",]

#outcome = c("Final Pitch", "Appropriateness of Methodology", "Weekly Evaluation", "Commitment", "Attendance", "Deliverables", "Sum", "Total")

df_corr = data.frame()

for (i in colnames(outcome))
{
  if(! i %in% c("Team", "Stage"))
  {
    for (j in colnames(network_stats))
    {
      if(!j %in% c("nodes"))
      {
        c = cor.test(temp[,j], temp[,i])
        
        df_corr = rbind(df_corr, data.frame(i = i, j = j, cor = c$estimate, p_val = c$p.value))
        
      }
    }
  }
}


df_corr$cor[df_corr$p_val > 0.1] = 0
df_corr$cor = round(df_corr$cor, 3)

plt = ggplot(df_corr) + geom_tile(aes(x = i, y = j, fill = cor), lwd = 1.5, linetype = 1) + scale_fill_gradient2(low = "blue", high = "red") + theme_bw() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank()) + ylab("") + xlab("") + geom_text(aes(x = i, y = j, label = cor))

ggplotly(plt)
```

Diversity

```{r}

temp = merge(metrics[,c(1:17)], outcome, by.x = "Team", by.y = "Team")
df_corr = data.frame()

for (i in colnames(outcome))
{
  if(! i %in% c("Team", "Stage"))
  {
    for (j in colnames(metrics))
    {
      if(!j %in% c("Team", "Type", "Stage"))
      {
        c = cor.test(temp[,j], temp[,i])
        
        df_corr = rbind(df_corr, data.frame(i = i, j = j, cor = c$estimate, p_val = c$p.value))
        
      }
    }
  }
}

df_corr$cor[df_corr$p_val > 0.1] = 0
df_corr$cor = round(df_corr$cor, 3)

plt = ggplot(df_corr) + geom_tile(aes(x = i, y = j, fill = cor), lwd = 1.5, linetype = 1) + scale_fill_gradient2(low = "blue", high = "red") + theme_bw() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank()) + ylab("") + xlab("") + geom_text(aes(x = i, y = j, label = cor))

ggplotly(plt)

```



*******************************************************************************

Some Teamwise Network Properties

```{r}

inter = interactions[,c(1,8,2,3)]
inter = merge(inter, map[,c("ID", "Team")], by.x = "user_id", by.y = "ID", all.x = TRUE)
colnames(inter) = c("From", "To", "Survey_id", "Question", "From_team")
inter = merge(inter, map[,c("ID", "Team")], by.x = "To", by.y = "ID", all.x = TRUE)
colnames(inter)[colnames(inter) == "Team"] = "To_team"

inter = inter[!inter$To %in% c(34), c(2,1,3,4,5,6)]

g_int_teams = graph_from_data_frame(inter[,c(5,6,1:4)], directed = TRUE)
E(g_int_teams)$weight = 1
g_int_teams_simp = simplify(g_int_teams, remove.loops = FALSE)


```

Network Metrics

1. Weighted interactions (Other teams, self, ORG)
2. Normalised weighted interactions - total = 1


```{r}

process_interactions = function(inter)
{
  temp = inter %>% group_by(From_team, To_team) %>% summarise(weight = n())
  temp_out = temp %>% group_by(From_team) %>% summarise(self = weight[To_team == From_team], org_out = weight[To_team == "Organizing Team"], peers_out = sum(weight[!To_team %in% c(From_team, "Organizing Team")]))

  temp_in = temp %>% group_by(To_team) %>% summarise(peers_in = sum(weight[!From_team == To_team]))

  degr = merge(temp_out, temp_in, by.x = "From_team", by.y = "To_team", all.x = TRUE, all.y = TRUE)
  colnames(degr)[colnames(degr) == "From_team"] = "Team"

  degr = degr[!degr$Team == "Organizing Team",]
  return(degr)
}

```



```{r}

degr = process_interactions(inter)
#degr = merge(degr, reg_map, by.x = "Team", by.y = "old_name")

degr = merge(degr, assesment, by.x = "new_name", by.y = "Team", all.x = TRUE, all.y = TRUE)
degr_as = merge(degr, teams, by.x = "new_name", by.y = "Team Name")

```


Pre-Formed vs Assembled

i) All Interactions

```{r}

pdf("../figures/stats_team_type_box.pdf")

for (i in colnames(degr_as))
{
  if(!i %in% c("new_name", "Team", "Type", "Geographic Location", "Gender F/M", "Stage"))
  {
    temp = degr_as[,c("Type", i)]
    colnames(temp) = c("Type", "var")
    #t = temp %>% group_by(Type) %>% summarise(mean = mean(var), se = se(var))
    
    plt = ggplot(temp, aes(x = Type, y = var)) + geom_boxplot() + theme_bw(base_size = 20) + xlab("") + ylab("") + ggtitle(i) + geom_point(aes(x = Type, y = var), alpha = 0.3)
    
    print(plt)
    
  }
}

dev.off()


pdf("../figures/stats_team_type_bar.pdf")

for (i in colnames(degr_as))
{
  if(!i %in% c("new_name", "Team", "Type", "Geographic Location", "Gender F/M", "Stage"))
  {
    temp = degr_as[,c("Type", i)]
    colnames(temp) = c("Type", "var")
    t = temp %>% group_by(Type) %>% summarise(mean = mean(var), se = se(var))
    
    plt = ggplot(t, aes(x = Type, y = mean)) + geom_bar(stat = "identity") + geom_errorbar(aes(ymin = mean-se, ymax = mean+se), width = 0) + theme_bw(base_size = 20) + xlab("") + ylab("") + ggtitle(i)
    
    print(plt)
    
  }
}

dev.off()
```

ii) Specific Interactions

```{r}


for (j in unique(inter$Question))
{  
  
  a = process_interactions(inter[inter$Question == j,])
  a = merge(a, reg_map, by.x = "Team", by.y = "old_name")
  a = merge(a, teams, by.x = "new_name", by.y = "Team Name")
  
  pdf(paste("../figures/", j, "_box.pdf", sep = ""))
  
  for (i in colnames(a))
  {
    if(!i %in% c("new_name", "Team", "Type", "Stage"))
    {
      temp = a[,c("Type", i)]
      colnames(temp) = c("Type", "var")
      plt = ggplot(temp, aes(x = Type, y = var)) + geom_boxplot() + theme_bw(base_size = 20) + xlab("") + ylab("") + ggtitle(i) + geom_point(aes(x = Type, y = var), alpha = 0.3)
      
      print(plt)
      
    }
  }
  
  dev.off()
  
pdf(paste("../figures/", j, "_bar.pdf", sep = ""))

for (i in colnames(a))
{
  if(!i %in% c("new_name", "Team", "Type", "Geographic Location", "Gender F/M", "Stage"))
  {
    temp = a[,c("Type", i)]
    colnames(temp) = c("Type", "var")
    t = temp %>% group_by(Type) %>% summarise(mean = mean(var), se = se(var))
    
    plt = ggplot(t, aes(x = Type, y = mean)) + geom_bar(stat = "identity") + geom_errorbar(aes(ymin = mean-se, ymax = mean+se), width = 0) + theme_bw(base_size = 20) + xlab("") + ylab("") + ggtitle(i)
    
    print(plt)
    
  }
}

dev.off()

}
```



***********************************************************************************************


```{r}

pdf("../figures/stats_team_progress_box.pdf")

for (i in colnames(degr_as))
{
  if(!i %in% c("new_name", "Team", "Type", "Geographic Location", "Gender F/M", "Stage"))
  {
    temp = degr_as[,c("Stage", i)]
    colnames(temp) = c("Type", "var")
    #t = temp %>% group_by(Type) %>% summarise(mean = mean(var), se = se(var))
    
    plt = ggplot(temp, aes(x = Type, y = var)) + geom_boxplot() + theme_bw(base_size = 20) + xlab("") + ylab("") + ggtitle(i) + geom_point(aes(x = Type, y = var), alpha = 0.3)
    
    print(plt)
    
  }
}

dev.off()


pdf("../figures/stats_team_progress_bar.pdf")

for (i in colnames(degr_as))
{
  if(!i %in% c("new_name", "Team", "Type", "Geographic Location", "Gender F/M", "Stage"))
  {
    temp = degr_as[,c("Stage", i)]
    colnames(temp) = c("Type", "var")
    t = temp %>% group_by(Type) %>% summarise(mean = mean(var), se = se(var))
    
    plt = ggplot(t, aes(x = Type, y = mean)) + geom_bar(stat = "identity") + geom_errorbar(aes(ymin = mean-se, ymax = mean+se), width = 0) + theme_bw(base_size = 20) + xlab("") + ylab("") + ggtitle(i)
    
    print(plt)
    
  }
}

dev.off()
```
